Background
Prenatal diagnosis of placenta accreta spectrum (PAS) disorders is difficult. Magnetic resonance imaging (MRI) has been shown to be a useful supplementary method to ultrasound.
Purpose
To investigate diffusion MRI (dMRI) based tractography as a tool for detecting PAS disorders, and to evaluate its performance compared with anatomical MRI.
Study Type
Prospective.
Population
Forty‐seven pregnant women in the third trimester with risk factors for PAS.
Field Strength/Sequence
Using fast imaging employing steady‐state acquisition and high‐angular resolution dMRI at 1.5 Tesla.
Assessment
Diagnosis of PAS was performed by three radiologists based on the dMRI‐based feature of myometrial fiber discontinuity and on commonly used anatomical features including presence of dark band, discontinuous myometrium and bladder wall interruption. We evaluated the sensitivity, specificity, accuracy, and area‐under‐the‐curve (AUC) of the individual features and established an integrated model with random forest analysis.
Statistical Tests
Maternal age and gestational age at scan were compared between PAS and control group using a t‐test, and childbearing history was compared using a chi‐squared test. The random forest model was employed to combine the anatomical and dMRI features with 5‐fold cross‐validation, and the weight of each feature was normalized to evaluate its importance in predicting PAS.
Results
Based on surgical pathology reports, 16 out of 47 patients had confirmed PAS. The anatomical feature of dark bands and tractography marker achieved the highest AUC of 0.842 for predicting PAS, and the integrated anatomical and tractography features further improved the AUC of 0.880 with an accuracy of 87.2%. The tractography feature contributed most (30.1%) to the integrated model.
Data Conclusion
Myometrial tractography demonstrated superior performance in detecting PAS. Moreover, the combination of dMRI‐based tractography and anatomical MRI could potentially improve the diagnosis of PAS disorders in clinical practice.
Level of Evidence
2
Technical Efficacy Stage
2